{"id":"W2075584011","doi":"10.1109/tsp.2014.2388437","title":"Incremental Grassmannian Feedback Schemes for Multi-User MIMO Systems","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Codebook; Vector quantization; Grassmannian; Quantization (signal processing); MIMO; Algorithm; Fading; Mathematics; Robustness (evolution); Computer science; Multiple description coding; Channel state information; Decoding methods; Theoretical computer science; Channel (broadcasting); Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004687296,0.0003818315,0.0003304046,0.0002246369,0.0003402183,0.000384059,0.0008557384,0.0003303652,0.00144391],"category_scores_gemma":[0.001314885,0.0001314944,0.0001899989,0.0004121778,0.0004893864,0.0007485592,0.0005689917,0.0005397887,0.0002344164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005989236,"about_ca_system_score_gemma":0.0005781788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002476383,"about_ca_topic_score_gemma":0.003550331,"domain_scores_codex":[0.9997211,0.00006785215,0.0000144658,0.00003727933,0.0001274637,0.00003182424],"domain_scores_gemma":[0.9996572,0.0001322697,0.00005078292,0.00006777441,0.00007012374,0.00002190926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001986401,0.00006500105,0.0003810376,0.0001227999,0.0000273895,0.0001606812,0.000248291,0.6463986,0.02443698,0.1045351,0.002452401,0.2209731],"study_design_scores_gemma":[0.00001433495,0.0000931764,0.0001217744,0.000006733076,0.00000525531,0.00004389102,0.00001416909,0.9797192,0.003418238,0.01503591,0.001510915,0.00001649727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02566648,0.0006205753,0.970509,0.0001165484,0.00005970151,0.00004167893,0.00004928808,0.0003664872,0.002570287],"genre_scores_gemma":[0.8758698,0.0004285912,0.1213221,0.00009056104,0.00004627426,0.00005838067,0.00005483394,0.00002389667,0.002105576],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002476383,"threshold_uncertainty_score":0.00492394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04653462945431597,"score_gpt":0.278141585542208,"score_spread":0.231606956087892,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}